
R version 3.0.2 (2013-09-25) -- "Frisbee Sailing"
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> set.seed(2423045)
> rm(list=ls())
> library(Matching)
Loading required package: MASS
## 
##  Matching (Version 4.8-3.4, Build Date: 2013/10/28)
##  See http://sekhon.berkeley.edu/matching for additional documentation.
##  Please cite software as:
##   Jasjeet S. Sekhon. 2011. ``Multivariate and Propensity Score Matching
##   Software with Automated Balance Optimization: The Matching package for R.''
##   Journal of Statistical Software, 42(7): 1-52. 
##

> 
> load("rep.sourcecode.re74psid1.RData")
> 
> X <-   as.matrix(cbind( foo$age,
+                         foo$educ,
+                         foo$black,
+                         foo$hispan,
+                         foo$married,
+                         foo$nodegree,
+                         I(foo$re74/1000),
+                         #I(as.real(foo$re74 == 0)),
+                         I(foo$re75/1000)))# ,
>                         #I(as.real(foo$re75 == 0))))
>                  
> BalanceMat <- as.matrix(cbind(
+                      foo$age,
+                      foo$educ,
+                      foo$black,
+                      foo$hispan,
+                      foo$married,
+                      foo$nodegree,
+                      I(foo$re74/1000),
+                      I(foo$re75/1000),
+                      I((foo$re74/1000)^2),
+                      I((foo$re75/1000)^2),
+                          
+                      I((foo$age)^2),
+                      I((foo$educ)^2),
+                      I((foo$black)^2),
+                      I((foo$hispan)^2),
+                      I((foo$married)^2),
+                      I((foo$nodegree)^2),
+                          
+                      I(foo$age*foo$educ),
+                      I(foo$age*foo$black),
+                      I(foo$age*foo$hispan),
+                      I(foo$age*foo$married),    
+                      I(foo$age*foo$nodegree),
+                      I(foo$age*(foo$re74/1000)),
+                      I(foo$age*(foo$re75/1000)),
+ 
+                      I(foo$educ*foo$black),
+                      I(foo$educ*foo$hispan),
+                      I(foo$educ*foo$married),    
+                      I(foo$educ*foo$nodegree),
+                      I(foo$educ*(foo$re74/1000)),
+                      I(foo$educ*(foo$re75/1000)),
+ 
+ #                     I(foo$black*foo$hispan), This is always zero!
+                      I(foo$black*foo$married),
+                      I(foo$black*foo$nodegree),
+                      I(foo$black*(foo$re74/1000)),
+                      I(foo$black*(foo$re75/1000)),
+ 
+                      I(foo$hispan*foo$married),
+                      I(foo$hispan*foo$nodegree),
+                      I(foo$hispan*(foo$re74/1000)),
+                      I(foo$hispan*(foo$re75/1000)),
+ 
+                      I(foo$married*foo$nodegree),
+                      I(foo$married*(foo$re74/1000)),
+                      I(foo$married*(foo$re75/1000)),
+                                                 
+                      I(foo$nodegree*(foo$re74/1000)),
+                      I(foo$nodegree*(foo$re75/1000)),                        
+ 
+                      I((foo$re74/1000)*(foo$re75/1000))                                                 
+                          ) )
> 
> 
> # From Review of Econ and Statistics
> model.A = foo$treat~I(foo$age) + I(foo$age^2) + I(foo$age^3) + I(foo$educ) + I(foo$educ^2) + I(foo$married) + I(foo$nodegree) + I(foo$black) + I(foo$hispan) + I(foo$re74) + I(foo$re75) + I(foo$re74 == 0) + I(foo$re75 == 0) + I(I(foo$re74)*I(foo$educ))
> 
> pscores.A <- glm(model.A, family = binomial)
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred 
> 
> X2 <- cbind(X,BalanceMat[,9:ncol(BalanceMat)]) #first 8 spots of BalanceMat are already in X
> for (i in 1:ncol(X2))
+   {
+     lm1 = lm(X2[,i]~pscores.A$linear.pred)
+     X2[,i] = lm1$residual
+    }
> 
> # VARIABLES WE MATCH ON BEGIN WITH PROPENSITY SCORE
> orthoX2.plus.pscore = cbind(pscores.A$linear.pred, X2)
> orthoX2.plus.pscore[,1] <- orthoX2.plus.pscore[,1] -  mean(orthoX2.plus.pscore[,1])
> 
> 
> # NORMALIZE ALL X COVARS BY STANDARD DEVIATION
> #for (i in 1:(ncol(X)+1))
> for (i in 1:ncol(X2))
+   {
+     orthoX2.plus.pscore[,i] <-
+       orthoX2.plus.pscore[,i]/sqrt(var(orthoX2.plus.pscore[,i]))
+   }
> 
> 
> 
> #from psid1.re74.gm4.triton1.Rout
> sv <- c(295.511000014901, 14.2662500149012, 0.0082209559011612,
+         896.706500014901, 15.0243600149012, 0.228082912914503,
+         495.687727940869, 13.4455100149012, 529.198600014901,
+         3.59451701490116, 1.49011611938477e-08, 1.30349069030950,
+         1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.16125698111802, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.49011611938477e-08, 484.152800014901, 1.49011611938477e-08,
+         40.8400600149012, 1.49011611938477e-08, 1.75958844981308,
+         1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08,
+         1.49011611938477e-08, 1.49011611938477e-08)
> 
> #orthoX2.plus.pscore <- orthoX2.plus.pscore[,1:(8+cnew)]
> 
> GM.out <-  GenMatch(Tr = foo$treat,
+                     X = orthoX2.plus.pscore,
+                     starting.values=sv,
+                     BalanceMatrix = BalanceMat,
+                     pop.size = 1,
+                     max.generations = 1,
+                     hard.generation.limit=TRUE,
+                     wait.generations = 1)
Loading required package: rgenoud
Loading required package: parallel
##  rgenoud (Version 5.7-12, Build Date: 2013-06-28)
##  See http://sekhon.berkeley.edu/rgenoud for additional documentation.
##  Please cite software as:
##   Walter Mebane, Jr. and Jasjeet S. Sekhon. 2011.
##   ``Genetic Optimization Using Derivatives: The rgenoud package for R.''
##   Journal of Statistical Software, 42(11): 1-26. 
##



Sun Nov 17 16:43:30 2013
Domains:
 0.000000e+00   <=  X1   <=    1.000000e+03 
 0.000000e+00   <=  X2   <=    1.000000e+03 
 0.000000e+00   <=  X3   <=    1.000000e+03 
 0.000000e+00   <=  X4   <=    1.000000e+03 
 0.000000e+00   <=  X5   <=    1.000000e+03 
 0.000000e+00   <=  X6   <=    1.000000e+03 
 0.000000e+00   <=  X7   <=    1.000000e+03 
 0.000000e+00   <=  X8   <=    1.000000e+03 
 0.000000e+00   <=  X9   <=    1.000000e+03 
 0.000000e+00   <=  X10  <=    1.000000e+03 
 0.000000e+00   <=  X11  <=    1.000000e+03 
 0.000000e+00   <=  X12  <=    1.000000e+03 
 0.000000e+00   <=  X13  <=    1.000000e+03 
 0.000000e+00   <=  X14  <=    1.000000e+03 
 0.000000e+00   <=  X15  <=    1.000000e+03 
 0.000000e+00   <=  X16  <=    1.000000e+03 
 0.000000e+00   <=  X17  <=    1.000000e+03 
 0.000000e+00   <=  X18  <=    1.000000e+03 
 0.000000e+00   <=  X19  <=    1.000000e+03 
 0.000000e+00   <=  X20  <=    1.000000e+03 
 0.000000e+00   <=  X21  <=    1.000000e+03 
 0.000000e+00   <=  X22  <=    1.000000e+03 
 0.000000e+00   <=  X23  <=    1.000000e+03 
 0.000000e+00   <=  X24  <=    1.000000e+03 
 0.000000e+00   <=  X25  <=    1.000000e+03 
 0.000000e+00   <=  X26  <=    1.000000e+03 
 0.000000e+00   <=  X27  <=    1.000000e+03 
 0.000000e+00   <=  X28  <=    1.000000e+03 
 0.000000e+00   <=  X29  <=    1.000000e+03 
 0.000000e+00   <=  X30  <=    1.000000e+03 
 0.000000e+00   <=  X31  <=    1.000000e+03 
 0.000000e+00   <=  X32  <=    1.000000e+03 
 0.000000e+00   <=  X33  <=    1.000000e+03 
 0.000000e+00   <=  X34  <=    1.000000e+03 
 0.000000e+00   <=  X35  <=    1.000000e+03 
 0.000000e+00   <=  X36  <=    1.000000e+03 
 0.000000e+00   <=  X37  <=    1.000000e+03 
 0.000000e+00   <=  X38  <=    1.000000e+03 
 0.000000e+00   <=  X39  <=    1.000000e+03 
 0.000000e+00   <=  X40  <=    1.000000e+03 
 0.000000e+00   <=  X41  <=    1.000000e+03 
 0.000000e+00   <=  X42  <=    1.000000e+03 
 0.000000e+00   <=  X43  <=    1.000000e+03 
 0.000000e+00   <=  X44  <=    1.000000e+03 
NOTE: population size is not an even number
      increasing population size by 1

Data Type: Floating Point
Operators (code number, name, population) 
	(1) Cloning........................... 	1
	(2) Uniform Mutation.................. 	0
	(3) Boundary Mutation................. 	0
	(4) Non-Uniform Mutation.............. 	0
	(5) Polytope Crossover................ 	0
	(6) Simple Crossover.................. 	0
	(7) Whole Non-Uniform Mutation........ 	0
	(8) Heuristic Crossover............... 	0
	(9) Local-Minimum Crossover........... 	0

HARD Maximum Number of Generations: 1
Maximum Nonchanging Generations: 1
Population size       : 2
Convergence Tolerance: 1.000000e-03

Not Using the BFGS Derivative Based Optimizer on the Best Individual Each Generation.
Not Checking Gradients before Stopping.
Using Out of Bounds Individuals.

Maximization Problem.
GENERATION: 0 (initializing the population)
Lexical Fit..... 2.928444e-02  3.370224e-02  3.370224e-02  3.370224e-02  3.505982e-02  3.963896e-02  4.488107e-02  4.488107e-02  5.279540e-02  5.847025e-02  5.847025e-02  6.347110e-02  6.766790e-02  6.916380e-02  7.714114e-02  7.714114e-02  7.714114e-02  8.473841e-02  8.560718e-02  8.655755e-02  8.987178e-02  9.393624e-02  9.956185e-02  1.229244e-01  1.636585e-01  1.927860e-01  2.009407e-01  2.009407e-01  2.485482e-01  2.621240e-01  2.772978e-01  2.840063e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.391431e-01  3.802497e-01  4.540036e-01  4.570437e-01  5.187205e-01  5.326685e-01  6.137350e-01  6.612980e-01  6.701613e-01  6.701613e-01  6.870912e-01  6.870912e-01  6.870912e-01  6.870912e-01  7.709674e-01  7.709674e-01  7.905319e-01  8.474885e-01  8.474885e-01  8.475868e-01  8.475868e-01  8.475868e-01  8.475868e-01  8.942900e-01  9.087619e-01  9.109761e-01  9.372090e-01  9.569647e-01  9.842611e-01  9.962552e-01  9.962552e-01  9.995561e-01  9.999842e-01  9.999842e-01  9.999842e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 2, #Total UniqueCount: 2
var 1:
best............ 2.955110e+02
mean............ 3.526873e+02
variance........ 3.269131e+03
var 2:
best............ 1.426625e+01
mean............ 1.302989e+02
variance........ 1.346358e+04
var 3:
best............ 8.220956e-03
mean............ 2.313154e+02
variance........ 5.350299e+04
var 4:
best............ 8.967065e+02
mean............ 9.226412e+02
variance........ 6.726088e+02
var 5:
best............ 1.502436e+01
mean............ 1.568729e+02
variance........ 2.012102e+04
var 6:
best............ 2.280829e-01
mean............ 1.639947e+02
variance........ 2.681952e+04
var 7:
best............ 4.956877e+02
mean............ 4.993904e+02
variance........ 1.370974e+01
var 8:
best............ 1.344551e+01
mean............ 1.525868e+02
variance........ 1.936031e+04
var 9:
best............ 5.291986e+02
mean............ 3.472173e+02
variance........ 3.311720e+04
var 10:
best............ 3.594517e+00
mean............ 4.951270e+02
variance........ 2.416041e+05
var 11:
best............ 1.490116e-08
mean............ 1.391168e+02
variance........ 1.935348e+04
var 12:
best............ 1.303491e+00
mean............ 5.442684e+01
variance........ 2.822090e+03
var 13:
best............ 1.490116e-08
mean............ 3.935601e+02
variance........ 1.548895e+05
var 14:
best............ 1.490116e-08
mean............ 4.150312e+02
variance........ 1.722509e+05
var 15:
best............ 1.490116e-08
mean............ 1.367825e+02
variance........ 1.870946e+04
var 16:
best............ 1.490116e-08
mean............ 4.286513e+01
variance........ 1.837420e+03
var 17:
best............ 1.490116e-08
mean............ 4.850065e+02
variance........ 2.352313e+05
var 18:
best............ 1.490116e-08
mean............ 4.389421e+02
variance........ 1.926702e+05
var 19:
best............ 1.490116e-08
mean............ 4.679109e+02
variance........ 2.189406e+05
var 20:
best............ 1.490116e-08
mean............ 4.636040e+02
variance........ 2.149286e+05
var 21:
best............ 1.490116e-08
mean............ 4.292097e+02
variance........ 1.842209e+05
var 22:
best............ 1.490116e-08
mean............ 4.292564e+02
variance........ 1.842611e+05
var 23:
best............ 1.490116e-08
mean............ 1.807966e+02
variance........ 3.268740e+04
var 24:
best............ 1.490116e-08
mean............ 3.559244e+02
variance........ 1.266822e+05
var 25:
best............ 1.490116e-08
mean............ 3.042453e+02
variance........ 9.256523e+04
var 26:
best............ 1.490116e-08
mean............ 9.639613e+01
variance........ 9.292213e+03
var 27:
best............ 1.490116e-08
mean............ 3.454237e+02
variance........ 1.193175e+05
var 28:
best............ 1.161257e+00
mean............ 8.327448e+01
variance........ 6.742582e+03
var 29:
best............ 1.490116e-08
mean............ 1.092183e+01
variance........ 1.192865e+02
var 30:
best............ 1.490116e-08
mean............ 2.975437e+02
variance........ 8.853228e+04
var 31:
best............ 1.490116e-08
mean............ 2.827773e+02
variance........ 7.996298e+04
var 32:
best............ 1.490116e-08
mean............ 9.500623e+01
variance........ 9.026183e+03
var 33:
best............ 1.490116e-08
mean............ 4.464600e+02
variance........ 1.993265e+05
var 34:
best............ 1.490116e-08
mean............ 2.264560e+02
variance........ 5.128231e+04
var 35:
best............ 4.841528e+02
mean............ 3.276598e+02
variance........ 2.449006e+04
var 36:
best............ 1.490116e-08
mean............ 4.807189e+02
variance........ 2.310906e+05
var 37:
best............ 4.084006e+01
mean............ 4.891660e+02
variance........ 2.009962e+05
var 38:
best............ 1.490116e-08
mean............ 4.808962e+02
variance........ 2.312611e+05
var 39:
best............ 1.759588e+00
mean............ 8.095576e+01
variance........ 6.272034e+03
var 40:
best............ 1.490116e-08
mean............ 4.430343e+01
variance........ 1.962794e+03
var 41:
best............ 1.490116e-08
mean............ 4.945547e+02
variance........ 2.445843e+05
var 42:
best............ 1.490116e-08
mean............ 1.544317e+02
variance........ 2.384916e+04
var 43:
best............ 1.490116e-08
mean............ 4.376513e+02
variance........ 1.915386e+05
var 44:
best............ 1.490116e-08
mean............ 8.904741e+01
variance........ 7.929441e+03

GENERATION: 1
Lexical Fit..... 2.928444e-02  3.370224e-02  3.370224e-02  3.370224e-02  3.505982e-02  3.963896e-02  4.488107e-02  4.488107e-02  5.279540e-02  5.847025e-02  5.847025e-02  6.347110e-02  6.766790e-02  6.916380e-02  7.714114e-02  7.714114e-02  7.714114e-02  8.473841e-02  8.560718e-02  8.655755e-02  8.987178e-02  9.393624e-02  9.956185e-02  1.229244e-01  1.636585e-01  1.927860e-01  2.009407e-01  2.009407e-01  2.485482e-01  2.621240e-01  2.772978e-01  2.840063e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.391431e-01  3.802497e-01  4.540036e-01  4.570437e-01  5.187205e-01  5.326685e-01  6.137350e-01  6.612980e-01  6.701613e-01  6.701613e-01  6.870912e-01  6.870912e-01  6.870912e-01  6.870912e-01  7.709674e-01  7.709674e-01  7.905319e-01  8.474885e-01  8.474885e-01  8.475868e-01  8.475868e-01  8.475868e-01  8.475868e-01  8.942900e-01  9.087619e-01  9.109761e-01  9.372090e-01  9.569647e-01  9.842611e-01  9.962552e-01  9.962552e-01  9.995561e-01  9.999842e-01  9.999842e-01  9.999842e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  
#unique......... 0, #Total UniqueCount: 2
var 1:
best............ 2.955110e+02
mean............ 3.526873e+02
variance........ 3.269131e+03
var 2:
best............ 1.426625e+01
mean............ 1.302989e+02
variance........ 1.346358e+04
var 3:
best............ 8.220956e-03
mean............ 2.313154e+02
variance........ 5.350299e+04
var 4:
best............ 8.967065e+02
mean............ 9.226412e+02
variance........ 6.726088e+02
var 5:
best............ 1.502436e+01
mean............ 1.568729e+02
variance........ 2.012102e+04
var 6:
best............ 2.280829e-01
mean............ 1.639947e+02
variance........ 2.681952e+04
var 7:
best............ 4.956877e+02
mean............ 4.993904e+02
variance........ 1.370974e+01
var 8:
best............ 1.344551e+01
mean............ 1.525868e+02
variance........ 1.936031e+04
var 9:
best............ 5.291986e+02
mean............ 3.472173e+02
variance........ 3.311720e+04
var 10:
best............ 3.594517e+00
mean............ 4.951270e+02
variance........ 2.416041e+05
var 11:
best............ 1.490116e-08
mean............ 1.391168e+02
variance........ 1.935348e+04
var 12:
best............ 1.303491e+00
mean............ 5.442684e+01
variance........ 2.822090e+03
var 13:
best............ 1.490116e-08
mean............ 3.935601e+02
variance........ 1.548895e+05
var 14:
best............ 1.490116e-08
mean............ 4.150312e+02
variance........ 1.722509e+05
var 15:
best............ 1.490116e-08
mean............ 1.367825e+02
variance........ 1.870946e+04
var 16:
best............ 1.490116e-08
mean............ 4.286513e+01
variance........ 1.837420e+03
var 17:
best............ 1.490116e-08
mean............ 4.850065e+02
variance........ 2.352313e+05
var 18:
best............ 1.490116e-08
mean............ 4.389421e+02
variance........ 1.926702e+05
var 19:
best............ 1.490116e-08
mean............ 4.679109e+02
variance........ 2.189406e+05
var 20:
best............ 1.490116e-08
mean............ 4.636040e+02
variance........ 2.149286e+05
var 21:
best............ 1.490116e-08
mean............ 4.292097e+02
variance........ 1.842209e+05
var 22:
best............ 1.490116e-08
mean............ 4.292564e+02
variance........ 1.842611e+05
var 23:
best............ 1.490116e-08
mean............ 1.807966e+02
variance........ 3.268740e+04
var 24:
best............ 1.490116e-08
mean............ 3.559244e+02
variance........ 1.266822e+05
var 25:
best............ 1.490116e-08
mean............ 3.042453e+02
variance........ 9.256523e+04
var 26:
best............ 1.490116e-08
mean............ 9.639613e+01
variance........ 9.292213e+03
var 27:
best............ 1.490116e-08
mean............ 3.454237e+02
variance........ 1.193175e+05
var 28:
best............ 1.161257e+00
mean............ 8.327448e+01
variance........ 6.742582e+03
var 29:
best............ 1.490116e-08
mean............ 1.092183e+01
variance........ 1.192865e+02
var 30:
best............ 1.490116e-08
mean............ 2.975437e+02
variance........ 8.853228e+04
var 31:
best............ 1.490116e-08
mean............ 2.827773e+02
variance........ 7.996298e+04
var 32:
best............ 1.490116e-08
mean............ 9.500623e+01
variance........ 9.026183e+03
var 33:
best............ 1.490116e-08
mean............ 4.464600e+02
variance........ 1.993265e+05
var 34:
best............ 1.490116e-08
mean............ 2.264560e+02
variance........ 5.128231e+04
var 35:
best............ 4.841528e+02
mean............ 3.276598e+02
variance........ 2.449006e+04
var 36:
best............ 1.490116e-08
mean............ 4.807189e+02
variance........ 2.310906e+05
var 37:
best............ 4.084006e+01
mean............ 4.891660e+02
variance........ 2.009962e+05
var 38:
best............ 1.490116e-08
mean............ 4.808962e+02
variance........ 2.312611e+05
var 39:
best............ 1.759588e+00
mean............ 8.095576e+01
variance........ 6.272034e+03
var 40:
best............ 1.490116e-08
mean............ 4.430343e+01
variance........ 1.962794e+03
var 41:
best............ 1.490116e-08
mean............ 4.945547e+02
variance........ 2.445843e+05
var 42:
best............ 1.490116e-08
mean............ 1.544317e+02
variance........ 2.384916e+04
var 43:
best............ 1.490116e-08
mean............ 4.376513e+02
variance........ 1.915386e+05
var 44:
best............ 1.490116e-08
mean............ 8.904741e+01
variance........ 7.929441e+03

Solution Lexical Fitness Value:
2.928444e-02  3.370224e-02  3.370224e-02  3.370224e-02  3.505982e-02  3.963896e-02  4.488107e-02  4.488107e-02  5.279540e-02  5.847025e-02  5.847025e-02  6.347110e-02  6.766790e-02  6.916380e-02  7.714114e-02  7.714114e-02  7.714114e-02  8.473841e-02  8.560718e-02  8.655755e-02  8.987178e-02  9.393624e-02  9.956185e-02  1.229244e-01  1.636585e-01  1.927860e-01  2.009407e-01  2.009407e-01  2.485482e-01  2.621240e-01  2.772978e-01  2.840063e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.173158e-01  3.391431e-01  3.802497e-01  4.540036e-01  4.570437e-01  5.187205e-01  5.326685e-01  6.137350e-01  6.612980e-01  6.701613e-01  6.701613e-01  6.870912e-01  6.870912e-01  6.870912e-01  6.870912e-01  7.709674e-01  7.709674e-01  7.905319e-01  8.474885e-01  8.474885e-01  8.475868e-01  8.475868e-01  8.475868e-01  8.475868e-01  8.942900e-01  9.087619e-01  9.109761e-01  9.372090e-01  9.569647e-01  9.842611e-01  9.962552e-01  9.962552e-01  9.995561e-01  9.999842e-01  9.999842e-01  9.999842e-01  9.999999e-01  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  

Parameters at the Solution:

 X[ 1] :	2.955110e+02
 X[ 2] :	1.426625e+01
 X[ 3] :	8.220956e-03
 X[ 4] :	8.967065e+02
 X[ 5] :	1.502436e+01
 X[ 6] :	2.280829e-01
 X[ 7] :	4.956877e+02
 X[ 8] :	1.344551e+01
 X[ 9] :	5.291986e+02
 X[10] :	3.594517e+00
 X[11] :	1.490116e-08
 X[12] :	1.303491e+00
 X[13] :	1.490116e-08
 X[14] :	1.490116e-08
 X[15] :	1.490116e-08
 X[16] :	1.490116e-08
 X[17] :	1.490116e-08
 X[18] :	1.490116e-08
 X[19] :	1.490116e-08
 X[20] :	1.490116e-08
 X[21] :	1.490116e-08
 X[22] :	1.490116e-08
 X[23] :	1.490116e-08
 X[24] :	1.490116e-08
 X[25] :	1.490116e-08
 X[26] :	1.490116e-08
 X[27] :	1.490116e-08
 X[28] :	1.161257e+00
 X[29] :	1.490116e-08
 X[30] :	1.490116e-08
 X[31] :	1.490116e-08
 X[32] :	1.490116e-08
 X[33] :	1.490116e-08
 X[34] :	1.490116e-08
 X[35] :	4.841528e+02
 X[36] :	1.490116e-08
 X[37] :	4.084006e+01
 X[38] :	1.490116e-08
 X[39] :	1.759588e+00
 X[40] :	1.490116e-08
 X[41] :	1.490116e-08
 X[42] :	1.490116e-08
 X[43] :	1.490116e-08
 X[44] :	1.490116e-08

Solution Found Generation 1
Number of Generations Run 1

Sun Nov 17 16:43:30 2013
Total run time : 0 hours 0 minutes and 0 seconds
> 
> sv=diag(GM.out$Weight.matrix)
> cat("Starting Values:\n")
Starting Values:
> write.csv(sv,eol=", ", row.names=FALSE)
"x", 295.511000014901, 14.2662500149012, 0.0082209559011612, 896.706500014901, 15.0243600149012, 0.228082912914503, 495.687727940869, 13.4455100149012, 529.198600014901, 3.59451701490116, 1.49011611938477e-08, 1.3034906903095, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.16125698111802, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 484.152800014901, 1.49011611938477e-08, 40.8400600149012, 1.49011611938477e-08, 1.75958844981308, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, 1.49011611938477e-08, > 
> mout <- Match(Y=foo$re78, 
+               Tr = foo$treat,
+               X = orthoX2.plus.pscore,
+               Weight.matrix=GM.out)
> summary(mout)

Estimate...  1049.9 
AI SE......  1736.9 
T-stat.....  0.60445 
p.val......  0.54554 

Original number of observations..............  2675 
Original number of treated obs...............  185 
Matched number of observations...............  185 
Matched number of observations  (unweighted).  192 

> 
> 
> 
> proc.time()
   user  system elapsed 
  1.404   0.074   1.564 
